Overall Offensive Load Carried (SportVU Aided)

I'm pretty happy with the way the "isolated rim protection" analysis I've been noodling with works as a broad measure of that particular area of defensive achievement. It also translates NBA.com's SportVU numbers into a more easily digestible data point. Most of the numbers are interesting, but without context don't really explain whether the actions tracked are good, or indifferent as far as positive contributions on the floor. For that reason, I've been on the lookout for more ways to try and interpret the publicly available data, perhaps in combination with other, more traditional stats to provide that context. One area where I think this is possible is "offensive load carried."

Typically, we simply refer to the size of a player's role in an offense by noting their usage statistic. And as far as it goes, this is great - we know that on a "perfectly balanced" team, all 5 players will have a USG of 20%, so we can tell some things about how much or little a player shoots by comparing their usage to this average. Under 20% usage players tend to be either limited spot up shooters or bangers with little game outside of putbacks. Above 25% represents primary options and above 30% are the true ball-dominant chuckers.

However, usage presents a slightly distorted picture of how offense (especially a high functioning offense) tends to work. If a point guard splits a double team on a pick and roll, euro-steps past the help defender, draws the rim protector into the air and drops a nifty pass off to a grinder big man who happened to be cutting to the basket for an offensive rebound, that possession was "used" and that shot "created" by the big man. As with many basketball statistical questions this gets us into the difficult area of parsing individual credit from events which are undoubtedly positive on the team level.

I'm not going to untie that whole knot here. In fact, I'm going to basically skipping the question of whom to credit by saying "why not both?" I think it meshes with simple observation that there is no reason a basketball possession has to be a one man show, though it certainly can be.

So to that end, I've been messing around with a series of metrics to measure overall offensive load carried by each player. Basically, I'm combining traditional usage with TOs and SportVU defined assist chances, and expressing it as a percentage of a team's offensive possessions a player is "directly involved in" while on the floor.

This has also allowed me to express turnovers in a rate stat which encompasses both shooting and play making - traditional TOV% tends to overcredit shoot first players as the only "positive" outcome in terms of reducing TOV% is shooting the ball. Merely not turning the ball over (while for example passing to a shooter) is a neutral event which has no effect on TOV%, which has the counter-intuitive effect of making the players who presumably take the best care of the ball (pass-first PGs) look like the most profligate with it. To put it another way, a measure of taking care of the ball that makes prime Steve Nash look bad in terms of handling the rock has serious issues.

Since I'm translating basic USG% into another metric, I wanted to also include a TS% equivalent number, so I included a "Equivalent FG%" of each individuals "plays involved". Alternatively, double the number listed to get a PPP.

Since the data seemed to naturally break down this way anyway, I've separated players into "PG's, Wings, and Bigs". Their is no position adjustment involved, it's simply a grouping of players. There are 43 PGs, 93 wings and 72 bigs included.

For the data, I used all players who through the games of Monday the 6th had played at least 16 games (more or less half the games played) and at least 20 minutes per game. This left 208 players. I think it's probable that this biased the overall league averages are probably slightly lower than the sample I used as I think minute distribution is roughly rational - better players play more. Better players handle the ball more, so the guys not selected in the sample probably handle the ball/shoot less on a per possession basis, but that's neither here nor there as the numbers aren't scaled to any average.

The three columns are

OffLoad - the percentage of plays the player is "directly involved in" either as shooter, passer or "hockey assister" while on the floor. Average across the sample was just under 35%, which aligns with about 1.73 players "involved" per play on a team level which feels slightly high, but not ridiculous. Would DEFINITELY accept suggestions for a better name for this one...

TO/Load - Reconfigured TO% representing percentage of turnovers in total plays including shots AND assist attempts. Average for the sample was 8.76%

PlayEFG - Expressing the efficiency of possessions in which the player is involved in a shooting percentage-style notation. Alternatively, double the number for a PPP/ORTG style number. More on this below, but I want to strongly caveat the tables below that I don't think this number should carry much weight in evaluating individual players.

First the PG's sorted from highest load to lowest

Player

OffLoad

To/Load

PlayEFG

Russell Westbrook (OKC)

65.58%

8.80%

0.623

John Wall (WAS)

63.83%

7.57%

0.583

Chris Paul (LAC)

61.32%

5.71%

0.594

Deron Williams (BKN)

60.58%

7.67%

0.672

Stephen Curry (GSW)

58.80%

9.14%

0.612

Michael Carter-Williams (PHI)

57.74%

7.97%

0.668

Ty Lawson (DEN)

57.46%

7.60%

0.561

Brandon Jennings (DET)

56.76%

7.89%

0.546

Kyrie Irving (CLE)

55.65%

7.61%

0.545

Steve Blake (LAL)

55.06%

6.86%

0.661

Raymond Felton (NYK)

54.93%

6.11%

0.664

Jeff Teague (ATL)

54.90%

9.31%

0.514

Eric Bledsoe (PHX)

53.99%

8.84%

0.652

Brandon Knight (MIL)

53.43%

8.73%

0.553

Tony Parker (SAS)

53.26%

6.90%

0.547

Mike Conley (MEM)

53.03%

5.75%

0.575

Trey Burke (UTA)

52.53%

5.85%

0.567

Isaiah Thomas (SAC)

52.25%

7.88%

0.543

Jrue Holiday (NOP)

50.96%

8.94%

0.554

Jordan Farmar (LAL)

50.58%

10.12%

0.504

Goran Dragic (PHX)

50.34%

7.61%

0.632

Kyle Lowry (TOR)

47.82%

6.28%

0.612

Damian Lillard (POR)

47.51%

6.87%

0.599

Kemba Walker (CHA)

46.98%

7.87%

0.509

Jameer Nelson (ORL)

45.26%

8.27%

0.529

Ricky Rubio (MIN)

44.93%

9.15%

0.534

Jordan Crawford (BOS)

43.43%

8.16%

0.512

Kirk Hinrich (CHI)

41.67%

7.64%

0.518

Greivis Vasquez (TOTAL)

40.97%

8.97%

0.427

Jeremy Lin (HOU)

40.58%

11.23%

0.645

Reggie Jackson (OKC)

39.36%

9.75%

0.440

Ramon Sessions (CHA)

38.56%

9.01%

0.361

Mo Williams (POR)

37.81%

11.65%

0.444

Luke Ridnour (MIL)

37.70%

7.75%

0.493

Jarrett Jack (CLE)

36.34%

8.88%

0.450

Nate Wolters (MIL)

35.16%

7.37%

0.471

Beno Udrih (NYK)

34.95%

10.57%

0.450

Mario Chalmers (MIA)

33.91%

11.26%

0.554

Jose Calderon (DAL)

33.67%

5.55%

0.601

George Hill (IND)

33.26%

6.31%

0.566

Shaun Livingston (BKN)

30.97%

9.40%

0.438

Patrick Beverley (HOU)

28.30%

6.57%

0.604

Norris Cole (MIA)

28.07%

11.85%

0.435

The wings:

Player

OffLoad

To/Load

PlayEFG

James Harden (HOU)

51.94%

9.48%

0.642

LeBron James (MIA)

51.52%

9.35%

0.655

Dwyane Wade (MIA)

48.64%

8.92%

0.663

Monta Ellis (DAL)

48.27%

9.40%

0.563

Kevin Durant (OKC)

47.62%

8.31%

0.630

Carmelo Anthony (NYK)

47.09%

6.19%

0.643

Luol Deng (CHI)

46.24%

7.71%

0.717

Gordon Hayward (UTA)

44.97%

8.19%

0.501

Paul George (IND)

44.25%

8.14%

0.586

Tyreke Evans (NOP)

44.01%

9.17%

0.482

DeMar DeRozan (TOR)

43.15%

7.35%

0.574

Bradley Beal (WAS)

42.53%

7.38%

0.686

Tony Wroten (PHI)

42.24%

11.58%

0.428

Rudy Gay (TOTAL)

42.06%

10.86%

0.544

Evan Turner (PHI)

41.85%

10.01%

0.527

Manu Ginobili (SAS)

40.62%

10.07%

0.492

Dion Waiters (CLE)

39.90%

10.86%

0.502

Arron Afflalo (ORL)

39.86%

7.30%

0.605

Victor Oladipo (ORL)

39.44%

13.15%

0.416

Caron Butler (MIL)

39.17%

9.03%

0.623

Louis Williams (ATL)

38.99%

7.67%

0.566

Rodney Stuckey (DET)

37.52%

9.10%

0.485

Paul Pierce (BKN)

37.14%

11.99%

0.514

Lance Stephenson (IND)

37.03%

10.05%

0.579

Jamal Crawford (LAC)

36.88%

8.24%

0.445

Eric Gordon (NOP)

36.56%

8.97%

0.533

J.R. Smith (NYK)

36.48%

6.64%

0.525

Alec Burks (UTA)

36.26%

8.91%

0.399

Gary Neal (MIL)

36.15%

8.96%

0.428

O.J. Mayo (MIL)

35.87%

11.13%

0.442

JJ Redick (LAC)

35.84%

4.76%

0.858

Gerald Henderson (CHA)

35.61%

6.55%

0.476

Nicolas Batum (POR)

35.35%

10.12%

0.593

Kevin Martin (MIN)

35.24%

7.19%

0.549

Andre Iguodala (GSW)

35.23%

9.16%

0.753

Joe Johnson (BKN)

34.42%

5.67%

0.547

Nick Young (LAL)

34.14%

7.94%

0.440

Vince Carter (DAL)

33.60%

8.93%

0.426

Jimmy Butler (CHI)

32.99%

7.21%

0.692

Chandler Parsons (HOU)

32.88%

7.84%

0.636

Jerryd Bayless (MEM)

32.30%

5.36%

0.406

Trevor Ariza (WAS)

32.22%

8.56%

0.663

Klay Thompson (GSW)

31.94%

8.84%

0.581

Jeff Green (BOS)

31.00%

9.30%

0.494

Avery Bradley (BOS)

30.60%

8.96%

0.439

Tony Allen (MEM)

30.57%

11.54%

0.507

Xavier Henry (LAL)

30.17%

10.31%

0.356

Wesley Matthews (POR)

28.76%

6.00%

0.609

Gerald Green (PHX)

28.42%

9.61%

0.477

Mike Dunleavy (CHI)

28.23%

7.88%

0.495

Wilson Chandler (DEN)

27.90%

5.75%

0.572

Khris Middleton (MIL)

27.70%

8.34%

0.481

Matt Barnes (LAC)

27.70%

10.02%

0.557

Jeremy Lamb (OKC)

27.07%

5.98%

0.398

Randy Foye (DEN)

26.88%

8.82%

0.436

Marco Belinelli (SAS)

26.87%

8.90%

0.464

Marcus Morris (PHX)

26.67%

9.63%

0.387

Ray Allen (MIA)

26.57%

9.85%

0.538

Michael Kidd-Gilchrist (CHA)

26.42%

12.63%

0.640

Harrison Barnes (GSW)

25.94%

7.71%

0.510

Kawhi Leonard (SAS)

25.93%

7.98%

0.483

Richard Jefferson (UTA)

25.90%

10.21%

0.437

Jeff Taylor (CHA)

25.74%

7.99%

0.404

Shawn Marion (DAL)

25.35%

7.93%

0.486

Jodie Meeks (LAL)

25.24%

8.59%

0.514

Marcus Thornton (SAC)

24.93%

5.98%

0.462

John Salmons (TOTAL)

24.83%

6.54%

0.454

Omri Casspi (HOU)

24.69%

10.43%

0.355

Kyle Korver (ATL)

24.24%

7.59%

0.677

Tayshaun Prince (MEM)

24.18%

5.78%

0.440

Martell Webster (WAS)

24.03%

4.72%

0.614

Ben McLemore (SAC)

23.74%

9.79%

0.381

Terrence Ross (TOR)

23.55%

8.99%

0.395

Corey Brewer (MIN)

23.47%

9.81%

0.500

Giannis Antetokounmpo (MIL)

23.38%

12.63%

0.446

PJ Tucker (PHX)

23.37%

9.63%

0.480

James Anderson (PHI)

23.33%

10.54%

0.443

Iman Shumpert (NYK)

23.29%

8.85%

0.457

Mike Miller (MEM)

22.06%

9.19%

0.438

Al-Farouq Aminu (NOP)

21.93%

9.19%

0.446

DeMarre Carroll (ATL)

21.61%

7.58%

0.517

Danny Green (SAS)

21.51%

10.18%

0.405

Alan Anderson (BKN)

21.20%

5.76%

0.424

Maurice Harkless (ORL)

20.90%

11.33%

0.328

Wesley Johnson (LAL)

20.07%

10.07%

0.429

Jared Dudley (LAC)

19.95%

8.52%

0.474

Gerald Wallace (BOS)

19.60%

20.87%

0.402

Kentavious Caldwell-Pope (DET)

19.52%

4.30%

0.395

Francisco Garcia (HOU)

19.39%

8.51%

0.363

Thabo Sefolosha (OKC)

19.27%

9.76%

0.448

Kyle Singler (DET)

18.58%

9.24%

0.397

Hollis Thompson (PHI)

16.65%

10.43%

0.374

Shane Battier (MIA)

13.74%

3.65%

0.416

The Bigs

Player

OffLoad

To/Load

PlayEFG

DeMarcus Cousins (SAC)

47.65%

11.36%

0.532

Kevin Love (MIN)

45.92%

7.00%

0.598

LaMarcus Aldridge (POR)

42.86%

4.98%

0.552

Pau Gasol (LAL)

41.61%

9.15%

0.477

Zach Randolph (MEM)

40.17%

9.63%

0.514

Blake Griffin (LAC)

39.67%

9.56%

0.529

Dirk Nowitzki (DAL)

39.02%

5.82%

0.566

Al Horford (ATL)

38.88%

8.48%

0.605

Brook Lopez (BKN)

38.82%

6.67%

0.966

Nene (WAS)

38.47%

8.38%

0.611

Al Jefferson (CHA)

36.95%

4.62%

0.587

Josh Smith (DET)

36.73%

9.28%

0.485

Tim Duncan (SAS)

35.51%

8.14%

0.497

Paul Millsap (ATL)

35.14%

9.97%

0.532

David West (IND)

34.91%

9.10%

0.502

Carlos Boozer (CHI)

34.67%

11.60%

0.463

Jared Sullinger (BOS)

34.38%

9.38%

0.421

Glen Davis (ORL)

33.90%

8.08%

0.616

David Lee (GSW)

33.56%

9.74%

0.539

Andray Blatche (BKN)

33.20%

10.83%

0.395

Zaza Pachulia (MIL)

33.09%

10.42%

0.607

Dwight Howard (HOU)

32.59%

14.68%

0.512

Thaddeus Young (PHI)

31.68%

7.82%

0.571

Spencer Hawes (PHI)

31.67%

10.62%

0.532

Joakim Noah (CHI)

31.51%

9.55%

0.533

Greg Monroe (DET)

31.33%

10.52%

0.486

Anthony Davis (NOP)

31.23%

6.46%

0.725

John Henson (MIL)

31.12%

8.63%

0.549

Andrea Bargnani (NYK)

30.75%

7.41%

0.441

Markieff Morris (PHX)

30.66%

11.21%

0.456

Ersan Ilyasova (MIL)

30.02%

10.83%

0.471

Ryan Anderson (NOP)

30.01%

4.15%

0.860

Josh McRoberts (CHA)

29.75%

6.38%

0.489

Taj Gibson (CHI)

29.41%

12.75%

0.421

Chris Bosh (MIA)

29.24%

7.95%

0.519

Nikola Vucevic (ORL)

28.95%

12.70%

0.571

Nikola Pekovic (MIN)

28.58%

7.93%

0.542

Enes Kanter (UTA)

28.46%

13.29%

0.358

Roy Hibbert (IND)

28.42%

12.37%

0.456

JJ Hickson (DEN)

28.16%

10.04%

0.410

Boris Diaw (SAS)

28.00%

9.77%

0.457

Derrick Favors (UTA)

27.95%

11.67%

0.471

Kevin Garnett (BKN)

26.86%

10.61%

0.358

Marcin Gortat (WAS)

26.76%

9.82%

0.535

Jason Smith (NOP)

25.77%

5.68%

0.521

Marvin Williams (UTA)

25.32%

7.60%

0.545

Kosta Koufos (MEM)

25.15%

10.35%

0.329

Serge Ibaka (OKC)

24.88%

9.02%

0.487

Tristan Thompson (CLE)

24.84%

9.86%

0.459

Jordan Hill (LAL)

24.77%

9.84%

0.369

Brandon Bass (BOS)

24.76%

8.37%

0.464

Tiago Splitter (SAS)

24.47%

12.41%

0.406

Channing Frye (PHX)

24.07%

8.41%

0.502

DeJuan Blair (DAL)

24.02%

13.27%

0.360

Jonas Valanciunas (TOR)

23.63%

13.31%

0.461

Terrence Jones (HOU)

23.60%

7.25%

0.462

Kenneth Faried (DEN)

23.46%

8.85%

0.412

Anderson Varejao (CLE)

23.35%

7.42%

0.476

Amir Johnson (TOR)

22.95%

12.48%

0.534

Miles Plumlee (PHX)

22.93%

11.54%

0.418

Patrick Patterson (TOTAL)

22.33%

9.85%

0.402

Trevor Booker (WAS)

21.75%

8.55%

0.536

Derrick Williams (SAC)

21.52%

8.67%

0.378

Andre Drummond (DET)

21.28%

10.12%

0.510

Jason Thompson (SAC)

20.78%

12.94%

0.408

Andrew Bogut (GSW)

20.11%

15.28%

0.461

Robin Lopez (POR)

19.07%

10.21%

0.490

Ekpe Udoh (MIL)

17.43%

14.13%

0.382

Shawne Williams (LAL)

16.57%

8.76%

0.377

DeAndre Jordan (LAC)

16.53%

13.46%

0.547

Samuel Dalembert (DAL)

16.48%

15.65%

0.385

Anthony Tolliver (CHA)

15.24%

8.06%

0.437

Some analysis and conjecture about the results.

OffLoad is purely descriptive, in a vacuum a higher percentage is not better or worse based on this data, it just is. Better offensive players tending to carry higher loads is a result of coaches, players and execs having eyes.

I like the way TO/Load tracks by player category, as it stands to reason that the guys who play PG are generally better at taking care of the ball than guys who play wing than the guys who play big becuase that's part of why they play those positions.

Raw USG substantially underrates the role of a PG or similar creator in a functioning offense.

Best guess is that players as a whole are overcredited with involvement and more so in terms of PlayEFG by generous assist giving. TheNick Van Exel homer assistprobably doesn't count as a "chance" in SportVU, so it only gets recorded if the shot goes in. So much in the same way getting fouled while shooting only helps a players FG% because it only counts as an attempt if it goes in, I think the EFG's are slightly high because of that factor.

That said, I don't think it matters terribly because A) it's a relative stat Luke Ridnour's .493 doesn't mean much except as compared to the league average of .536 and/or Nate Wolter's .471. But more importantly B) I don't want to put much stock at all in EFG as far as giving credit to the individual players as it's to a large degree a reflection of teammate quality rather than individual player talent to the extent player's "PlayEFG" diverges from TS%

All kinds of other interesting things show up here - Ryan Anderson has potential assists on just over 4% of New Orleans possessions while he is on the floor. I almost have to think this represents a failure in the design of their offense given how hard teams have to close out on him. EDIT: I actually misread my data, he actually has potential assists on only 2.91% of possessions he's on the floor. The only other players under 3% are Andre Drummond and Samuel Dalembert, so the point still holds I think.

So, what do people think?

I haven't attempted to perform any advanced statstical methodology on the data, first because my knowledge on that front gets me to linear regression, barely. But beyond that I worry at times that for all but the very few people who both require the exactitude provided by and either have the ability themselves or in their organization to translate the numbers into an understanding of what's happening on the court and vice versa, I worry that basketball analytics at times becomes too much about the numbers, and I wanted to try and come up with a measure that is both broadly understandable for fans with decent numeracy but not advanced mathematical knowledge and as first-pass accurate. Hopefully this has succeeded on at least one of those goals.